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Disentangling the dynamics of social assistance: A linked survey—Register data cohort study of long-term social assistance recipients in Norway

机译:解开社会援助的动态:挪威长期社会援助接受者的联系调查记录数据队列研究

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Social assistance is a means-tested benefit that is supposed to be a short-term, temporary economic support. Understanding why some individuals are in repeated or continuous need of social assistance is thus of obvious policy relevance, but the dynamics of social assistance receipt remain poorly understood. In 2005, a survey among long-term recipients of social assistance in Norway collected data on (a) childhood disadvantages, (b) health status, (c) health behaviors, (d) psychological resources, and (e) social ties, in addition to basic sociodemographic information. This rich survey data has been linked with tax register data from 2005–2013, enabling us to explore the detailed characteristics of long-term social assistance recipients who are unable to reach financial self-sufficiency. Results from linear probability models show that surprisingly few of the 28 explanatory variables are statistically associated with social assistance dynamics, with two important exceptions: People with drug problems and immigrants both have a much higher probability of social assistance receipt. Yet overall, it is challenging to ‘predict’ social assistance dynamics, indicating that randomness most likely plays a non-negligible role. The 28 explanatory variables do a far better job in predicting both labor market success (employment), labor market preparation (work assessment allowance), and labor market withdrawal (disability benefit utilization). Thus, there seems to be something distinctive about the processes leading to continued social assistance recipiency, where randomness could be a more influential force.
机译:社会援助是一种经过的手段受益,应该是短期,临时经济支持。因此,了解为什么有些人在重复或不断需要社会救助方面是明显的政策相关性,但社会救助收据的动态仍然明白。 2005年,挪威社会援助长期接受者的调查收集了关于(a)童年缺点,(b)卫生地位,(c)卫生行为,(d)心理资源和(e)社会领带的数据添加到基本的社会碘目信息。这种丰富的调查数据已与2005 - 2013年的税收寄存器数据有关,使我们能够探索无法达到财务自给自足的长期社会援助接受者的详细特征。线性概率模型的结果表明,28个解释性变量的令人惊讶的是与社会救助动态有统计相关,有两个重要的例外:毒品问题和移民的人们都有更高的社会救助收据概率。然而,整体而言,“预测”社会救助动态有挑战性,表明随机性最有可能发挥不可忽略的作用。 28解释性变量在预测劳动力市场成功(就业),劳动力市场准备(工作评估津贴)和劳动力市场撤离(残疾利益利用)方面做得更好。因此,似乎有些东西对导致持续社会救助的过程具有鲜明的过程,其中随机性可能是更有影响力的力量。

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